Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data.

In this research, we demonstrate the effectiveness of a convolutional neural network (CNN) model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in a mountainous watershed. Our methodology harnesses image-based inputs, incorporating spatial distribution maps of key environmental variables, including temperature, snowmelt, snow cover, snow depth, volumetric soil water content, total evaporation, total precipitation, and leaf area index. The proposed CNN architecture, while drawing inspiration from classical designs, is specifically tailored for the task of streamflow prediction.